\section{Results}
In testing and measuring our system we used a book called ``The Brave
Monkey Pirate''. It is a childrens book written by Robert Hayes
\cite{tbmp}.  The book is about a the brave monkey pirate named Modi,
who is scared of getting a shot from the doctor.


\subsection{Image-recognition}
The system is capable of extracting blobs from images, compare blobs
and cluster similar blobs in groups.
As you can see in figure~\ref{fig:blobs1} the blobs representing the
main character, Modi, of \cite{tbmp} have been clustered into the same
group. 
\begin{figure}[ht!]
  \begin{center}
    \includegraphics[scale=0.45]{group2/0.png}
    \includegraphics[scale=0.45]{group2/1.png}
    \includegraphics[scale=0.45]{group2/2.png}
    \includegraphics[scale=0.45]{group2/3.png}
    \includegraphics[scale=0.45]{group2/4.png}
    \includegraphics[scale=0.45]{group2/5.png}
    \includegraphics[scale=0.45]{group2/6.png}
	\caption{\label{fig:blobs1} Blobs of Modi grouped together. A human
instantly spots one blob that does not belong in the group - the fish
bowl.}
  \end{center}
\end{figure}

Figure~\ref{fig:blobs2} shows a group of blobs which for a human
does not seem to belong in the same group. This results depends on that
all these objects have similar colours in the way we compare them, each
colour channel for themselves and fairly similar proportions.

\begin{figure}[ht!]
  \begin{center}
    \includegraphics[scale=0.45]{group25/0.png}
    \includegraphics[scale=0.45]{group25/1.png}
    \includegraphics[scale=0.45]{group25/2.png}
    \includegraphics[scale=0.45]{group25/3.png}
    \includegraphics[scale=0.45]{group25/4.png}
    \includegraphics[scale=0.45]{group25/5.png}
    \includegraphics[scale=0.45]{group25/6.png}
	\caption{\label{fig:blobs2} Blobs of very different objects grouped
together.}
  \end{center}
\end{figure}

\subsection{Natural Language Processing}
The results in Table~\ref{tab:postaggers}
(page~\pageref{tab:postaggers}) clearly show that the BRAUBT tagger is
the most accurate. Even so, our current program is running with the,
\emph{off-the-shelf}, POS tagger. It turned out that the taggers we
wrote are bad at recognizing proper nouns\footnote{A solution for this
  could be to add a default tagger, which tags everything as nouns, as
  a backoff tagger at the ``end of the chain''.}, and the pre-trained
POS tagger had an acceptable accuracy on most of the sentences from
the texts we have been working with. Using the standard part-of-speech
tagger to identify nouns may very well be enough, based on earlier
attempts on creating Image Annotation systems \cite{makadia2008}.

\subsection{Reasoning System}
The system is very naive, but works quite well as long as there is a
fair (one-to-one is preferred) ratio between a blob and a word. That
is, if there is a picture of a sun, the word sun is expected to occur
about as often as the picture. However, in the instance of our book,
the system performs poorly. The book have many unique blobs, and
words. Thus it becomes very hard for the system to draw conclusions
because the dataset is simply to small.

It should however be mentioned that in one occurrence, the blob of
\emph{the doctor} was correctly extracted from several pages and was
consistently labelled with the correct word throughout the book.

The final product was able to achieve a \emph{precision} of about
2\%. This result showed us that achieving good results for annotating
images with words using unsupervised learning is no easy task.


